Overview

Dataset statistics

Number of variables22
Number of observations3726
Missing cells6869
Missing cells (%)8.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory659.5 B

Variable types

Categorical13
Numeric9

Alerts

sector has a high cardinality: 121 distinct valuesHigh cardinality
society has a high cardinality: 686 distinct valuesHigh cardinality
areaWithType has a high cardinality: 2386 distinct valuesHigh cardinality
price is highly overall correlated with price_per_sqft and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
area is highly overall correlated with price and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with price and 5 other fieldsHigh correlation
bathroom is highly overall correlated with price and 5 other fieldsHigh correlation
SuperBuiltUpArea is highly overall correlated with price and 7 other fieldsHigh correlation
builtUpArea is highly overall correlated with price and 3 other fieldsHigh correlation
carpetArea is highly overall correlated with price and 5 other fieldsHigh correlation
property_type is highly overall correlated with price and 3 other fieldsHigh correlation
balcony is highly overall correlated with property_typeHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
others is highly imbalanced (50.1%)Imbalance
store room is highly imbalanced (55.7%)Imbalance
facing has 1072 (28.8%) missing valuesMissing
SuperBuiltUpArea has 1845 (49.5%) missing valuesMissing
builtUpArea has 2005 (53.8%) missing valuesMissing
carpetArea has 1835 (49.2%) missing valuesMissing
area is highly skewed (γ1 = 29.83483292)Skewed
builtUpArea is highly skewed (γ1 = 36.47928394)Skewed
carpetArea is highly skewed (γ1 = 24.45605372)Skewed
luxury_score has 489 (13.1%) zerosZeros

Reproduction

Analysis started2025-07-05 19:41:47.662141
Analysis finished2025-07-05 19:41:53.299345
Duration5.64 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

sector
Categorical

Distinct121
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size270.3 KiB
sohna road
 
154
dlf phase
 
145
sector 85
 
108
sector 102
 
107
sector 92
 
99
Other values (116)
3113 

Length

Max length17
Median length9
Mean length9.2971014
Min length7

Characters and Unicode

Total characters34641
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsector 81
2nd rowsector 106
3rd rowdlf phase
4th rowsector 86
5th rowsector 36a

Common Values

ValueCountFrequency (%)
sohna road 154
 
4.1%
dlf phase 145
 
3.9%
sector 85 108
 
2.9%
sector 102 107
 
2.9%
sector 92 99
 
2.7%
sector 69 93
 
2.5%
sector 90 88
 
2.4%
sector 65 87
 
2.3%
sector 81 87
 
2.3%
sector 109 86
 
2.3%
Other values (111) 2672
71.7%

Length

2025-07-05T20:41:53.328459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sector 3266
43.8%
road 178
 
2.4%
sohna 166
 
2.2%
dlf 145
 
1.9%
phase 145
 
1.9%
85 108
 
1.4%
102 107
 
1.4%
92 99
 
1.3%
69 93
 
1.2%
90 88
 
1.2%
Other values (123) 3060
41.0%

Most occurring characters

ValueCountFrequency (%)
s 3736
10.8%
3729
10.8%
o 3710
10.7%
r 3524
10.2%
e 3472
10.0%
c 3356
9.7%
t 3353
9.7%
1 1082
 
3.1%
a 906
 
2.6%
0 796
 
2.3%
Other values (24) 6977
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24082
69.5%
Decimal Number 6830
 
19.7%
Space Separator 3729
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 3736
15.5%
o 3710
15.4%
r 3524
14.6%
e 3472
14.4%
c 3356
13.9%
t 3353
13.9%
a 906
 
3.8%
h 397
 
1.6%
d 395
 
1.6%
n 264
 
1.1%
Other values (13) 969
 
4.0%
Decimal Number
ValueCountFrequency (%)
1 1082
15.8%
0 796
11.7%
9 764
11.2%
8 756
11.1%
6 698
10.2%
7 670
9.8%
3 602
8.8%
2 540
7.9%
5 536
7.8%
4 386
 
5.7%
Space Separator
ValueCountFrequency (%)
3729
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24082
69.5%
Common 10559
30.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 3736
15.5%
o 3710
15.4%
r 3524
14.6%
e 3472
14.4%
c 3356
13.9%
t 3353
13.9%
a 906
 
3.8%
h 397
 
1.6%
d 395
 
1.6%
n 264
 
1.1%
Other values (13) 969
 
4.0%
Common
ValueCountFrequency (%)
3729
35.3%
1 1082
 
10.2%
0 796
 
7.5%
9 764
 
7.2%
8 756
 
7.2%
6 698
 
6.6%
7 670
 
6.3%
3 602
 
5.7%
2 540
 
5.1%
5 536
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 3736
10.8%
3729
10.8%
o 3710
10.7%
r 3524
10.2%
e 3472
10.0%
c 3356
9.7%
t 3353
9.7%
1 1082
 
3.1%
a 906
 
2.6%
0 796
 
2.3%
Other values (24) 6977
20.1%

property_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size251.9 KiB
flat
2841 
house
885 

Length

Max length5
Median length4
Mean length4.2375201
Min length4

Characters and Unicode

Total characters15789
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowhouse
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2841
76.2%
house 885
 
23.8%

Length

2025-07-05T20:41:53.371193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T20:41:53.422931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
flat 2841
76.2%
house 885
 
23.8%

Most occurring characters

ValueCountFrequency (%)
f 2841
18.0%
l 2841
18.0%
a 2841
18.0%
t 2841
18.0%
h 885
 
5.6%
o 885
 
5.6%
u 885
 
5.6%
s 885
 
5.6%
e 885
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15789
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2841
18.0%
l 2841
18.0%
a 2841
18.0%
t 2841
18.0%
h 885
 
5.6%
o 885
 
5.6%
u 885
 
5.6%
s 885
 
5.6%
e 885
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 15789
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2841
18.0%
l 2841
18.0%
a 2841
18.0%
t 2841
18.0%
h 885
 
5.6%
o 885
 
5.6%
u 885
 
5.6%
s 885
 
5.6%
e 885
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15789
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2841
18.0%
l 2841
18.0%
a 2841
18.0%
t 2841
18.0%
h 885
 
5.6%
o 885
 
5.6%
u 885
 
5.6%
s 885
 
5.6%
e 885
 
5.6%

society
Categorical

Distinct686
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size297.7 KiB
independent
510 
tulip violet
 
75
ss the leaf
 
73
shapoorji pallonji joyville gurugram
 
42
dlf new town heights
 
42
Other values (681)
2983 

Length

Max length49
Median length39
Mean length16.826577
Min length1

Characters and Unicode

Total characters62679
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique312 ?
Unique (%)8.4%

Sample

1st rowbestech park view grand spa
2nd rowparas dews
3rd rowindependent
4th rowdlf the skycourt
5th rowavl 36 gurgaon

Common Values

ValueCountFrequency (%)
independent 510
 
13.7%
tulip violet 75
 
2.0%
ss the leaf 73
 
2.0%
shapoorji pallonji joyville gurugram 42
 
1.1%
dlf new town heights 42
 
1.1%
signature global park 35
 
0.9%
shree vardhman victoria 34
 
0.9%
emaar mgf emerald floors premier 32
 
0.9%
smart world orchard 32
 
0.9%
dlf the ultima 31
 
0.8%
Other values (676) 2819
75.7%

Length

2025-07-05T20:41:53.474666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
independent 515
 
5.3%
the 354
 
3.6%
dlf 220
 
2.3%
park 209
 
2.1%
city 165
 
1.7%
emaar 155
 
1.6%
global 155
 
1.6%
m3m 152
 
1.6%
signature 150
 
1.5%
heights 134
 
1.4%
Other values (789) 7557
77.4%

Most occurring characters

ValueCountFrequency (%)
e 6819
 
10.9%
6043
 
9.6%
a 5910
 
9.4%
n 4259
 
6.8%
r 4198
 
6.7%
i 3877
 
6.2%
t 3765
 
6.0%
s 3500
 
5.6%
l 2967
 
4.7%
o 2766
 
4.4%
Other values (31) 18575
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56088
89.5%
Space Separator 6043
 
9.6%
Decimal Number 529
 
0.8%
Other Punctuation 11
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6819
12.2%
a 5910
 
10.5%
n 4259
 
7.6%
r 4198
 
7.5%
i 3877
 
6.9%
t 3765
 
6.7%
s 3500
 
6.2%
l 2967
 
5.3%
o 2766
 
4.9%
d 2536
 
4.5%
Other values (16) 15491
27.6%
Decimal Number
ValueCountFrequency (%)
3 207
39.1%
2 85
16.1%
1 74
 
14.0%
6 56
 
10.6%
8 32
 
6.0%
4 19
 
3.6%
5 17
 
3.2%
0 13
 
2.5%
9 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 8
72.7%
/ 2
 
18.2%
. 1
 
9.1%
Space Separator
ValueCountFrequency (%)
6043
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56088
89.5%
Common 6591
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6819
12.2%
a 5910
 
10.5%
n 4259
 
7.6%
r 4198
 
7.5%
i 3877
 
6.9%
t 3765
 
6.7%
s 3500
 
6.2%
l 2967
 
5.3%
o 2766
 
4.9%
d 2536
 
4.5%
Other values (16) 15491
27.6%
Common
ValueCountFrequency (%)
6043
91.7%
3 207
 
3.1%
2 85
 
1.3%
1 74
 
1.1%
6 56
 
0.8%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
0 13
 
0.2%
9 13
 
0.2%
Other values (5) 32
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6819
 
10.9%
6043
 
9.6%
a 5910
 
9.4%
n 4259
 
6.8%
r 4198
 
6.7%
i 3877
 
6.2%
t 3765
 
6.0%
s 3500
 
5.6%
l 2967
 
4.7%
o 2766
 
4.4%
Other values (31) 18575
29.6%

price
Real number (ℝ)

Distinct473
Distinct (%)12.8%
Missing37
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2.5261697
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.2 KiB
2025-07-05T20:41:53.532875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.51
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9759756
Coefficient of variation (CV)1.1780585
Kurtosis14.993735
Mean2.5261697
Median Absolute Deviation (MAD)0.72
Skewness3.2859554
Sum9319.04
Variance8.856431
MonotonicityNot monotonic
2025-07-05T20:41:53.587350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.1%
1.5 66
 
1.8%
1.2 64
 
1.7%
0.9 64
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 58
 
1.6%
0.95 54
 
1.4%
2 53
 
1.4%
1.6 49
 
1.3%
Other values (463) 3079
82.6%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

Distinct2666
Distinct (%)72.3%
Missing37
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean13882.194
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.2 KiB
2025-07-05T20:41:53.645277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4709.6
Q16812
median9011
Q313878
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7066

Descriptive statistics

Standard deviation23085.323
Coefficient of variation (CV)1.6629449
Kurtosis188.86555
Mean13882.194
Median Absolute Deviation (MAD)2794
Skewness11.467374
Sum51211414
Variance5.3293216 × 108
MonotonicityNot monotonic
2025-07-05T20:41:53.698983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 20
 
0.5%
5000 17
 
0.5%
12500 15
 
0.4%
22222 14
 
0.4%
6666 13
 
0.3%
11111 13
 
0.3%
7500 12
 
0.3%
8333 12
 
0.3%
6000 11
 
0.3%
Other values (2656) 3535
94.9%
(Missing) 37
 
1.0%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2647
Distinct (%)71.8%
Missing37
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2883.7152
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.2 KiB
2025-07-05T20:41:53.758221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile514.026
Q11224.13
median1728
Q32300.08
95-th percentile4267.532
Maximum875000
Range874950
Interquartile range (IQR)1075.95

Descriptive statistics

Standard deviation23079.964
Coefficient of variation (CV)8.0035518
Kurtosis948.91614
Mean2883.7152
Median Absolute Deviation (MAD)528.32
Skewness29.834833
Sum10638025
Variance5.3268473 × 108
MonotonicityNot monotonic
2025-07-05T20:41:53.812491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3240 37
 
1.0%
2000 33
 
0.9%
2700 32
 
0.9%
1800 27
 
0.7%
900 23
 
0.6%
1350 19
 
0.5%
1000 18
 
0.5%
1650.17 17
 
0.5%
1250 13
 
0.3%
2430 13
 
0.3%
Other values (2637) 3457
92.8%
(Missing) 37
 
1.0%
ValueCountFrequency (%)
50 5
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
 
0.1%
61 1
 
< 0.1%
67 2
 
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857.14 1
< 0.1%
620000 1
< 0.1%
566666.67 1
< 0.1%
215517.2 1
< 0.1%
98977.9 1
< 0.1%
82781.5 1
< 0.1%
65517.24 2
0.1%
65261 1
< 0.1%
58227.85 1
< 0.1%

areaWithType
Categorical

Distinct2386
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size433.3 KiB
Plot area 360(301.01 sq.m.)
 
37
Plot area 300(250.84 sq.m.)
 
26
Plot area 502(419.74 sq.m.)
 
19
Plot area 200(167.23 sq.m.)
 
19
Plot area 270(225.75 sq.m.)
 
17
Other values (2381)
3608 

Length

Max length124
Median length119
Mean length54.072195
Min length12

Characters and Unicode

Total characters201473
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1873 ?
Unique (%)50.3%

Sample

1st rowSuper Built up area 2660(247.12 sq.m.)
2nd rowCarpet area: 1385 (128.67 sq.m.)
3rd rowBuilt Up area: 360 (33.45 sq.m.)
4th rowSuper Built up area 1929(179.21 sq.m.)Built Up area: 1500 sq.ft. (139.35 sq.m.)Carpet area: 1300 sq.ft. (120.77 sq.m.)
5th rowSuper Built up area 1000(92.9 sq.m.)Carpet area: 727 sq.ft. (67.54 sq.m.)

Common Values

ValueCountFrequency (%)
Plot area 360(301.01 sq.m.) 37
 
1.0%
Plot area 300(250.84 sq.m.) 26
 
0.7%
Plot area 502(419.74 sq.m.) 19
 
0.5%
Plot area 200(167.23 sq.m.) 19
 
0.5%
Plot area 270(225.75 sq.m.) 17
 
0.5%
Super Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.) 17
 
0.5%
Super Built up area 1578(146.6 sq.m.) 17
 
0.5%
Plot area 900(83.61 sq.m.) 15
 
0.4%
Plot area 500(418.06 sq.m.) 15
 
0.4%
Super Built up area 1350(125.42 sq.m.) 15
 
0.4%
Other values (2376) 3529
94.7%

Length

2025-07-05T20:41:53.872200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
area 5636
18.5%
sq.m 3704
12.2%
up 3045
 
10.0%
built 2337
 
7.7%
super 1881
 
6.2%
sq.ft 1761
 
5.8%
sq.m.)carpet 1195
 
3.9%
sq.m.)built 706
 
2.3%
plot 698
 
2.3%
carpet 694
 
2.3%
Other values (2867) 8807
28.9%

Most occurring characters

ValueCountFrequency (%)
26738
 
13.3%
. 20602
 
10.2%
a 13305
 
6.6%
r 9550
 
4.7%
e 9410
 
4.7%
1 9280
 
4.6%
s 7648
 
3.8%
q 7508
 
3.7%
t 7397
 
3.7%
p 6819
 
3.4%
Other values (25) 83216
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 83578
41.5%
Decimal Number 47621
23.6%
Space Separator 26738
 
13.3%
Other Punctuation 23659
 
11.7%
Uppercase Letter 8681
 
4.3%
Close Punctuation 5598
 
2.8%
Open Punctuation 5598
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13305
15.9%
r 9550
11.4%
e 9410
11.3%
s 7648
9.2%
q 7508
9.0%
t 7397
8.9%
p 6819
8.2%
u 6807
8.1%
m 5607
6.7%
l 3743
 
4.5%
Other values (5) 5784
6.9%
Decimal Number
ValueCountFrequency (%)
1 9280
19.5%
0 6724
14.1%
2 5736
12.0%
5 4768
10.0%
3 3991
8.4%
4 3749
7.9%
6 3711
 
7.8%
7 3284
 
6.9%
8 3200
 
6.7%
9 3178
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3045
35.1%
C 1893
21.8%
S 1881
21.7%
U 1164
 
13.4%
P 698
 
8.0%
Other Punctuation
ValueCountFrequency (%)
. 20602
87.1%
: 3057
 
12.9%
Space Separator
ValueCountFrequency (%)
26738
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5598
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5598
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 109214
54.2%
Latin 92259
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13305
14.4%
r 9550
10.4%
e 9410
10.2%
s 7648
8.3%
q 7508
8.1%
t 7397
8.0%
p 6819
7.4%
u 6807
7.4%
m 5607
 
6.1%
l 3743
 
4.1%
Other values (10) 14465
15.7%
Common
ValueCountFrequency (%)
26738
24.5%
. 20602
18.9%
1 9280
 
8.5%
0 6724
 
6.2%
2 5736
 
5.3%
) 5598
 
5.1%
( 5598
 
5.1%
5 4768
 
4.4%
3 3991
 
3.7%
4 3749
 
3.4%
Other values (5) 16430
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26738
 
13.3%
. 20602
 
10.2%
a 13305
 
6.6%
r 9550
 
4.7%
e 9410
 
4.7%
1 9280
 
4.6%
s 7648
 
3.8%
q 7508
 
3.7%
t 7397
 
3.7%
p 6819
 
3.4%
Other values (25) 83216
41.3%

bedRoom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3604402
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.2 KiB
2025-07-05T20:41:53.921414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8940336
Coefficient of variation (CV)0.56362666
Kurtosis18.123419
Mean3.3604402
Median Absolute Deviation (MAD)1
Skewness3.4701945
Sum12521
Variance3.5873635
MonotonicityNot monotonic
2025-07-05T20:41:53.965721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1510
40.5%
2 960
25.8%
4 668
17.9%
5 213
 
5.7%
1 124
 
3.3%
6 77
 
2.1%
9 42
 
1.1%
7 30
 
0.8%
8 30
 
0.8%
12 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.3%
2 960
25.8%
3 1510
40.5%
4 668
17.9%
5 213
 
5.7%
6 77
 
2.1%
7 30
 
0.8%
8 30
 
0.8%
9 42
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4181428
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.2 KiB
2025-07-05T20:41:54.014407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9461567
Coefficient of variation (CV)0.56936085
Kurtosis17.429952
Mean3.4181428
Median Absolute Deviation (MAD)1
Skewness3.2333378
Sum12736
Variance3.7875258
MonotonicityNot monotonic
2025-07-05T20:41:54.057748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1087
29.2%
2 1069
28.7%
4 826
22.2%
5 295
 
7.9%
1 161
 
4.3%
6 119
 
3.2%
9 42
 
1.1%
7 41
 
1.1%
8 26
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 161
 
4.3%
2 1069
28.7%
3 1087
29.2%
4 826
22.2%
5 295
 
7.9%
6 119
 
3.2%
7 41
 
1.1%
8 26
 
0.7%
9 42
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size242.2 KiB
3+
1178 
3
932 
2
701 
1
292 
2.0
198 
Other values (3)
425 

Length

Max length3
Median length1
Mean length1.5496511
Min length1

Characters and Unicode

Total characters5774
Distinct characters6
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3+
2nd row2
3rd row0
4th row3+
5th row2

Common Values

ValueCountFrequency (%)
3+ 1178
31.6%
3 932
25.0%
2 701
18.8%
1 292
 
7.8%
2.0 198
 
5.3%
0 188
 
5.0%
3.0 152
 
4.1%
1.0 85
 
2.3%

Length

2025-07-05T20:41:54.108991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T20:41:54.167248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 2110
56.6%
2 701
 
18.8%
1 292
 
7.8%
2.0 198
 
5.3%
0 188
 
5.0%
3.0 152
 
4.1%
1.0 85
 
2.3%

Most occurring characters

ValueCountFrequency (%)
3 2262
39.2%
+ 1178
20.4%
2 899
 
15.6%
0 623
 
10.8%
. 435
 
7.5%
1 377
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4161
72.1%
Math Symbol 1178
 
20.4%
Other Punctuation 435
 
7.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2262
54.4%
2 899
 
21.6%
0 623
 
15.0%
1 377
 
9.1%
Math Symbol
ValueCountFrequency (%)
+ 1178
100.0%
Other Punctuation
ValueCountFrequency (%)
. 435
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5774
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2262
39.2%
+ 1178
20.4%
2 899
 
15.6%
0 623
 
10.8%
. 435
 
7.5%
1 377
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5774
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2262
39.2%
+ 1178
20.4%
2 899
 
15.6%
0 623
 
10.8%
. 435
 
7.5%
1 377
 
6.5%

facing
Categorical

Distinct8
Distinct (%)0.3%
Missing1072
Missing (%)28.8%
Memory size228.1 KiB
East
631 
North-East
629 
North
391 
West
249 
South
232 
Other values (3)
522 

Length

Max length10
Median length5
Mean length6.83685
Min length4

Characters and Unicode

Total characters18145
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth
2nd rowWest
3rd rowNorth-East
4th rowNorth-East
5th rowSouth

Common Values

ValueCountFrequency (%)
East 631
16.9%
North-East 629
16.9%
North 391
 
10.5%
West 249
 
6.7%
South 232
 
6.2%
North-West 194
 
5.2%
South-East 175
 
4.7%
South-West 153
 
4.1%
(Missing) 1072
28.8%

Length

2025-07-05T20:41:54.218458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T20:41:54.272390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
east 631
23.8%
north-east 629
23.7%
north 391
14.7%
west 249
 
9.4%
south 232
 
8.7%
north-west 194
 
7.3%
south-east 175
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3805
21.0%
s 2031
11.2%
o 1774
9.8%
h 1774
9.8%
E 1435
 
7.9%
a 1435
 
7.9%
N 1214
 
6.7%
r 1214
 
6.7%
- 1151
 
6.3%
W 596
 
3.3%
Other values (3) 1716
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13189
72.7%
Uppercase Letter 3805
 
21.0%
Dash Punctuation 1151
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3805
28.8%
s 2031
15.4%
o 1774
13.5%
h 1774
13.5%
a 1435
 
10.9%
r 1214
 
9.2%
e 596
 
4.5%
u 560
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
E 1435
37.7%
N 1214
31.9%
W 596
15.7%
S 560
 
14.7%
Dash Punctuation
ValueCountFrequency (%)
- 1151
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16994
93.7%
Common 1151
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3805
22.4%
s 2031
12.0%
o 1774
10.4%
h 1774
10.4%
E 1435
 
8.4%
a 1435
 
8.4%
N 1214
 
7.1%
r 1214
 
7.1%
W 596
 
3.5%
e 596
 
3.5%
Other values (2) 1120
 
6.6%
Common
ValueCountFrequency (%)
- 1151
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18145
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3805
21.0%
s 2031
11.2%
o 1774
9.8%
h 1774
9.8%
E 1435
 
7.9%
a 1435
 
7.9%
N 1214
 
6.7%
r 1214
 
6.7%
- 1151
 
6.3%
W 596
 
3.3%
Other values (3) 1716
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size285.2 KiB
Relatively New
1652 
New Property
599 
Moderately Old
571 
Undefined
321 
Old Property
312 

Length

Max length18
Median length14
Mean length13.371176
Min length9

Characters and Unicode

Total characters49821
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew Property
2nd rowNew Property
3rd rowUndefined
4th rowRelatively New
5th rowRelatively New

Common Values

ValueCountFrequency (%)
Relatively New 1652
44.3%
New Property 599
 
16.1%
Moderately Old 571
 
15.3%
Undefined 321
 
8.6%
Old Property 312
 
8.4%
Under Construction 271
 
7.3%

Length

2025-07-05T20:41:54.320831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T20:41:54.372705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
new 2251
31.6%
relatively 1652
23.2%
property 911
12.8%
old 883
 
12.4%
moderately 571
 
8.0%
undefined 321
 
4.5%
under 271
 
3.8%
construction 271
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8521
17.1%
l 4758
 
9.6%
t 3676
 
7.4%
3405
 
6.8%
y 3134
 
6.3%
r 2935
 
5.9%
d 2367
 
4.8%
N 2251
 
4.5%
w 2251
 
4.5%
i 2244
 
4.5%
Other values (15) 14279
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39285
78.9%
Uppercase Letter 7131
 
14.3%
Space Separator 3405
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8521
21.7%
l 4758
12.1%
t 3676
9.4%
y 3134
 
8.0%
r 2935
 
7.5%
d 2367
 
6.0%
w 2251
 
5.7%
i 2244
 
5.7%
a 2223
 
5.7%
o 2024
 
5.2%
Other values (7) 5152
13.1%
Uppercase Letter
ValueCountFrequency (%)
N 2251
31.6%
R 1652
23.2%
P 911
12.8%
O 883
 
12.4%
U 592
 
8.3%
M 571
 
8.0%
C 271
 
3.8%
Space Separator
ValueCountFrequency (%)
3405
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 46416
93.2%
Common 3405
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8521
18.4%
l 4758
 
10.3%
t 3676
 
7.9%
y 3134
 
6.8%
r 2935
 
6.3%
d 2367
 
5.1%
N 2251
 
4.8%
w 2251
 
4.8%
i 2244
 
4.8%
a 2223
 
4.8%
Other values (14) 12056
26.0%
Common
ValueCountFrequency (%)
3405
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8521
17.1%
l 4758
 
9.6%
t 3676
 
7.4%
3405
 
6.8%
y 3134
 
6.3%
r 2935
 
5.9%
d 2367
 
4.8%
N 2251
 
4.5%
w 2251
 
4.5%
i 2244
 
4.5%
Other values (15) 14279
28.7%

SuperBuiltUpArea
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct587
Distinct (%)31.2%
Missing1845
Missing (%)49.5%
Infinite0
Infinite (%)0.0%
Mean1923.8102
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.2 KiB
2025-07-05T20:41:54.430878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11478
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)737

Descriptive statistics

Standard deviation763.69512
Coefficient of variation (CV)0.39697009
Kurtosis10.352611
Mean1923.8102
Median Absolute Deviation (MAD)372
Skewness1.8387213
Sum3618687
Variance583230.23
MonotonicityNot monotonic
2025-07-05T20:41:54.566072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 37
 
1.0%
1950 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
2408 19
 
0.5%
1900 19
 
0.5%
1930 18
 
0.5%
2812 17
 
0.5%
Other values (577) 1640
44.0%
(Missing) 1845
49.5%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

builtUpArea
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct638
Distinct (%)37.1%
Missing2005
Missing (%)53.8%
Infinite0
Infinite (%)0.0%
Mean2596.6734
Minimum18
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.2 KiB
2025-07-05T20:41:54.622990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile260
Q11115
median1662
Q32430
95-th percentile4861
Maximum737147
Range737129
Interquartile range (IQR)1315

Descriptive statistics

Standard deviation18684.731
Coefficient of variation (CV)7.1956415
Kurtosis1404.2914
Mean2596.6734
Median Absolute Deviation (MAD)653
Skewness36.479284
Sum4468875
Variance3.4911918 × 108
MonotonicityNot monotonic
2025-07-05T20:41:54.679113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 42
 
1.1%
3240 38
 
1.0%
1350 34
 
0.9%
1900 34
 
0.9%
2700 33
 
0.9%
900 31
 
0.8%
1600 26
 
0.7%
2000 24
 
0.6%
1300 24
 
0.6%
1700 23
 
0.6%
Other values (628) 1412
37.9%
(Missing) 2005
53.8%
ValueCountFrequency (%)
18 1
 
< 0.1%
30 1
 
< 0.1%
50 4
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 3
0.1%
61 1
 
< 0.1%
62 1
 
< 0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
234000 1
 
< 0.1%
36000 1
 
< 0.1%
30600 2
 
0.1%
19440 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 9
0.2%
8775 1
 
< 0.1%

carpetArea
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct700
Distinct (%)37.0%
Missing1835
Missing (%)49.2%
Infinite0
Infinite (%)0.0%
Mean2516.5278
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.2 KiB
2025-07-05T20:41:54.736240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1830
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)960

Descriptive statistics

Standard deviation22685.512
Coefficient of variation (CV)9.0146082
Kurtosis610.68079
Mean2516.5278
Median Absolute Deviation (MAD)478
Skewness24.456054
Sum4758754
Variance5.1463245 × 108
MonotonicityNot monotonic
2025-07-05T20:41:54.791168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 36
 
1.0%
1600 35
 
0.9%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1000 23
 
0.6%
2000 22
 
0.6%
Other values (690) 1595
42.8%
(Missing) 1835
49.2%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76 3
0.1%
77 2
0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size240.2 KiB
0
3317 
1
409 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3726
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3317
89.0%
1 409
 
11.0%

Length

2025-07-05T20:41:54.840851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T20:41:54.885301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3317
89.0%
1 409
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3317
89.0%
1 409
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3726
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3317
89.0%
1 409
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3726
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3317
89.0%
1 409
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3317
89.0%
1 409
 
11.0%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size240.2 KiB
0
3063 
1
663 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3726
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3063
82.2%
1 663
 
17.8%

Length

2025-07-05T20:41:54.923508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T20:41:54.969484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3063
82.2%
1 663
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3063
82.2%
1 663
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3726
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3063
82.2%
1 663
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3726
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3063
82.2%
1 663
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3063
82.2%
1 663
 
17.8%

servant room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size240.2 KiB
0
2394 
1
1332 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3726
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2394
64.3%
1 1332
35.7%

Length

2025-07-05T20:41:55.007933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T20:41:55.051479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2394
64.3%
1 1332
35.7%

Most occurring characters

ValueCountFrequency (%)
0 2394
64.3%
1 1332
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3726
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2394
64.3%
1 1332
35.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3726
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2394
64.3%
1 1332
35.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2394
64.3%
1 1332
35.7%

store room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size240.2 KiB
0
3383 
1
343 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3726
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3383
90.8%
1 343
 
9.2%

Length

2025-07-05T20:41:55.090793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T20:41:55.136217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3383
90.8%
1 343
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3383
90.8%
1 343
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3726
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3383
90.8%
1 343
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3726
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3383
90.8%
1 343
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3383
90.8%
1 343
 
9.2%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size240.2 KiB
0
3017 
1
709 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3726
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3017
81.0%
1 709
 
19.0%

Length

2025-07-05T20:41:55.173152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T20:41:55.218659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3017
81.0%
1 709
 
19.0%

Most occurring characters

ValueCountFrequency (%)
0 3017
81.0%
1 709
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3726
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3017
81.0%
1 709
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3726
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3017
81.0%
1 709
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3017
81.0%
1 709
 
19.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size240.2 KiB
0
2479 
2
1044 
1
 
203

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3726
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2479
66.5%
2 1044
28.0%
1 203
 
5.4%

Length

2025-07-05T20:41:55.257200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-05T20:41:55.303333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 2479
66.5%
2 1044
28.0%
1 203
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 2479
66.5%
2 1044
28.0%
1 203
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3726
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2479
66.5%
2 1044
28.0%
1 203
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 3726
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2479
66.5%
2 1044
28.0%
1 203
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2479
66.5%
2 1044
28.0%
1 203
 
5.4%

luxury_score
Real number (ℝ)

Distinct161
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.863124
Minimum0
Maximum174
Zeros489
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size58.2 KiB
2025-07-05T20:41:55.350559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median58
Q3109.75
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)78.75

Descriptive statistics

Standard deviation53.139952
Coefficient of variation (CV)0.74989571
Kurtosis-0.87232025
Mean70.863124
Median Absolute Deviation (MAD)38
Skewness0.46783279
Sum264036
Variance2823.8545
MonotonicityNot monotonic
2025-07-05T20:41:55.406760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 489
 
13.1%
49 348
 
9.3%
174 195
 
5.2%
44 61
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
42 47
 
1.3%
60 47
 
1.3%
37 45
 
1.2%
Other values (151) 2332
62.6%
ValueCountFrequency (%)
0 489
13.1%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 31
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 11
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.2%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2025-07-05T20:41:52.424297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:48.805903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.275065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.731931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.157581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.610841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.136534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.553253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.991066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.471024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:48.863970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.325831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.777902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.206720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.659246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.182308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.602650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.037089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.522143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:48.914373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.376812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.825697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.256287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.710020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.230655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.653843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.087328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.568525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:48.961447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.422896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.867990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.302883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.755684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.274287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.700498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.136327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.621536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.016721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.477259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.917282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.356540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.807847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.325817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.752800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.188402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.673577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.070440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.531138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.966438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.408237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.859072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.372811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.805445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.237531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.721212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.121502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.578885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.012199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.458268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.905497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.417963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.846910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.283872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.771727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.174303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.630661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.060571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.510442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.956182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.459407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.896014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.327920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.822301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.224409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:49.680131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.108450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:50.560166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.083731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.505871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:51.938531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2025-07-05T20:41:52.375994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2025-07-05T20:41:55.464744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
priceprice_per_sqftareabedRoombathroomSuperBuiltUpAreabuiltUpAreacarpetArealuxury_scoreproperty_typebalconyfacingagePossessionotherspooja roomservant roomstore roomstudy roomfurnishing_type
price1.0000.7420.7460.6800.7200.7730.6380.6140.2200.5390.1670.0220.1020.0350.3350.3700.3060.2440.175
price_per_sqft0.7421.0000.2050.4190.4110.2890.1580.1380.0550.2060.0660.0000.0490.0310.0390.0490.0000.0320.021
area0.7460.2051.0000.6210.6860.9480.8600.8020.2640.0280.0000.0220.0000.0420.0370.0150.0380.0180.044
bedRoom0.6800.4190.6211.0000.8590.8000.4010.5680.0570.5900.1970.0330.1290.0770.2880.3150.2190.1520.166
bathroom0.7200.4110.6860.8591.0000.8190.4880.5970.1840.4670.2010.0430.1110.0670.2840.5190.2410.1740.194
SuperBuiltUpArea0.7730.2890.9480.8000.8191.0000.9260.8930.2241.0000.3060.0000.0860.0860.1570.5840.0410.1220.133
builtUpArea0.6380.1580.8600.4010.4880.9261.0000.9690.2860.0020.0300.0000.0000.0000.0290.0110.0000.0000.061
carpetArea0.6140.1380.8020.5680.5970.8930.9691.0000.2440.0000.0000.0000.0000.0160.0000.0000.0000.0050.000
luxury_score0.2200.0550.2640.0570.1840.2240.2860.2441.0000.3340.2010.0640.2570.1750.1910.3520.2270.1870.241
property_type0.5390.2060.0280.5900.4671.0000.0020.0000.3341.0000.7510.0920.3840.0240.2490.0580.2390.1230.081
balcony0.1670.0660.0000.1970.2010.3060.0300.0000.2010.7511.0000.0310.2940.0890.2410.4460.2030.1940.189
facing0.0220.0000.0220.0330.0430.0000.0000.0000.0640.0920.0311.0000.0920.0000.0240.0320.0320.0000.054
agePossession0.1020.0490.0000.1290.1110.0860.0000.0000.2570.3840.2940.0921.0000.1100.1880.2890.1480.1430.215
others0.0350.0310.0420.0770.0670.0860.0000.0160.1750.0240.0890.0000.1101.0000.0360.0000.1060.0350.064
pooja room0.3350.0390.0370.2880.2840.1570.0290.0000.1910.2490.2410.0240.1880.0361.0000.2540.3020.3170.215
servant room0.3700.0490.0150.3150.5190.5840.0110.0000.3520.0580.4460.0320.2890.0000.2541.0000.1600.1860.268
store room0.3060.0000.0380.2190.2410.0410.0000.0000.2270.2390.2030.0320.1480.1060.3020.1601.0000.2220.155
study room0.2440.0320.0180.1520.1740.1220.0000.0050.1870.1230.1940.0000.1430.0350.3170.1860.2221.0000.140
furnishing_type0.1750.0210.0440.1660.1940.1330.0610.0000.2410.0810.1890.0540.2150.0640.2150.2680.1550.1401.000

Missing values

2025-07-05T20:41:52.911165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-05T20:41:53.055058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-05T20:41:53.246501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

sectorproperty_typesocietypriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfacingagePossessionSuperBuiltUpAreabuiltUpAreacarpetAreaotherspooja roomservant roomstore roomstudy roomfurnishing_typeluxury_score
0sector 81flatbestech park view grand spa2.409022.02660.16Super Built up area 2660(247.12 sq.m.)343+NorthNew Property2660.0NaNNaN001012167
1sector 106flatparas dews1.057581.01385.04Carpet area: 1385 (128.67 sq.m.)222WestNew PropertyNaNNaN1385.000000097
2dlf phasehouseindependent4.50125000.0360.00Built Up area: 360 (33.45 sq.m.)440NaNUndefinedNaN360.0NaN0000000
3sector 86flatdlf the skycourt1.558035.01929.06Super Built up area 1929(179.21 sq.m.)Built Up area: 1500 sq.ft. (139.35 sq.m.)Carpet area: 1300 sq.ft. (120.77 sq.m.)333+North-EastRelatively New1929.01500.01300.0100001166
4sector 36aflatavl 36 gurgaon0.757500.01000.00Super Built up area 1000(92.9 sq.m.)Carpet area: 727 sq.ft. (67.54 sq.m.)222North-EastRelatively New1000.0NaN727.000000026
5sector 69flattulip violet1.9215360.01250.00Carpet area: 1250 (116.13 sq.m.)442SouthRelatively NewNaNNaN1250.0000000174
6sector 49flatorchid petals2.3811497.02070.11Super Built up area 2070(192.31 sq.m.)Built Up area: 2061 sq.ft. (191.47 sq.m.)Carpet area: 1750 sq.ft. (162.58 sq.m.)343WestRelatively New2070.02061.01750.000001049
7sector 95flatsignature global rosellia0.458754.0514.05Carpet area: 514 (47.75 sq.m.)222NaNNew PropertyNaNNaN514.000000024
8dlf phasehouseindependent6.6036667.01800.00Plot area 200(167.23 sq.m.)673+EastModerately OldNaN1800.0NaN001012119
9sector 113flatla vida by tata housing3.0511338.02690.07Super Built up area 2690(249.91 sq.m.)Built Up area: 2600 sq.ft. (241.55 sq.m.)Carpet area: 2240 sq.ft. (208.1 sq.m.)343North-EastNew Property2690.02600.02240.0001000174
sectorproperty_typesocietypriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfacingagePossessionSuperBuiltUpAreabuiltUpAreacarpetAreaotherspooja roomservant roomstore roomstudy roomfurnishing_typeluxury_score
3853sector 103housesatya the hermitage3.156702.04700.10Built Up area: 4700 (436.64 sq.m.)450EastUndefinedNaN4700.0NaN00000061
3854sector 33flatgodrej nature plus1.308349.01557.07Super Built up area 1557(144.65 sq.m.)323+NorthNew Property1557.0NaNNaN00000038
3855sector 78flatraheja revanta0.996100.01622.95Super Built up area 1621(150.6 sq.m.)223EastUnder Construction1621.0NaNNaN10000039
3857sector 102flatshapoorji pallonji joyville gurugram1.9010259.01852.03Super Built up area 1852(172.06 sq.m.)Built Up area: 1700 sq.ft. (157.94 sq.m.)Carpet area: 1450 sq.ft. (134.71 sq.m.)333+North-EastNew Property1852.01700.01450.0010000174
3858sector 9aflatthe new crpf apartments0.755813.01290.21Carpet area: 1290 (119.84 sq.m.)333EastOld PropertyNaNNaN1290.001000031
3859sector 49flatorchid petals2.5512318.02070.14Super Built up area 2070(192.31 sq.m.)Built Up area: 2060 sq.ft. (191.38 sq.m.)Carpet area: 1760 sq.ft. (163.51 sq.m.)343EastRelatively New2070.02060.01760.000001249
3860sector 70aflataipl the peaceful homes2.6012009.02165.04Carpet area: 2165 (201.14 sq.m.)322NaNModerately OldNaNNaN2165.000100052
3861sohna roadflateldeco accolade1.107549.01457.15Super Built up area 1457(135.36 sq.m.)323NaNRelatively New1457.0NaNNaN00000056
3862sector 37dflatbptp terra1.178297.01410.15Super Built up area 1410(130.99 sq.m.)222North-EastRelatively New1410.0NaNNaN000000107
3864sector 104flatats triumph1.737554.02290.18Super Built up area 2290(212.75 sq.m.)343+North-EastRelatively New2290.0NaNNaN001000159